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Exploring the Items for Measuring e-Procurement Usage
Construct: An Exploratory Factor Analysis
Suhaidi Elias, Veera Pandiyan A/L Kaliani Sundram and Noriah Ismail
To Link this Article: http://dx.doi.org/10.6007/IJARBSS/v10-i12/8164 DOI:10.6007/IJARBSS/v10-i12/8164
Received: 01 November 2020, Revised: 27 November 2020, Accepted: 17 December 2020
Published Online: 26 December 2020
In-Text Citation: (Elias et al., 2020)
To Cite this Article: Elias, S., Sundram, V. P. A. K., & Ismail, N. (2020). Exploring the Items for Measuring e-
Procurement Usage Construct: An Exploratory Factor Analysis. International Journal of Academic Research
in Business and Social Sciences, 10(12), 822–837.
Copyright: © 2020 The Author(s)
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Exploring the Items for Measuring e-Procurement
Usage Construct: An Exploratory Factor Analysis
Suhaidi Elias1
, Veera Pandiyan A/L Kaliani Sundram2
and
Noriah Ismail3
1, 2
Faculty of Business and Management, Universiti Teknologi MARA, Cawangan Johor,
Malaysia, 3
Academy of Language Study, Universiti Teknologi MARA, Cawangan Johor,
Malaysia
Email: suhaidi27@uitm.edu.my, veera692@uitm.edu.my, noriah135@uitm.edu.my
Abstract
The usage of Information system (IS) in any institution is vital and this human technology
interaction is the heart of many world-changing endeavors. The aim of this research is to
perform instrument validation through exploratory factor analysis (EFA) of using e-
procurement. The questionnaire used in this study is adapted from two different studies:
Venkatesh et al., (2012) and Norzaidi (2008). It consists of five sub-constructs; after the
questionnaire was distributed, 115 responses were collected to do the EFA. EFA was
done for each construct separately. The results show that all of the seven constructs have
one component or dimension, the factor loading for every item in each construct is >0.6,
Bartlett’s Test of Sphericity was <0.05 for all the constructs, which is Significant (P-value
< 0.05). Kaiser-Meyer-Olkin Measure of Sampling Adequacy was higher than 0.6 for all
the constructs, and this means that the sample size is adequate. Cronbach’s Alpha test
was higher than 0.7 for the entire constructs’ items, which means that these items are
all reliable. This study found a valid and reliable instrument for measuring the usage of
e-procurement to Malaysian contractors’.
Keywords: Exploratory Factor Analysis, Information System, E-Procurement Usage
Introduction
E-procurement is the use of integrated information technology for part of or all the
procurement functions, from the beginning to end, i.e. from searching, sourcing, negotiating,
ordering, and receipt to post-purchase review (Croom & Jones., 2005; Trkman & McCormack,
2010). E-procurement has been recently receiving much attention from businesses,
industries, and governments as it reportedly becomes a powerful tool to improve
effectiveness and efficiencies as well as the service quality of its adopters.
Malaysian government procurement is perceived as a major function of government
and a substantial amount of money is allocated annually for the procurement of goods and
services (Thai, 2001; Maniam et al., 2009). The Malaysian government spends more than
RM150 billion every year in procuring goods, works and services and this gives a sign of
riskiness of public procurement being exposed to corruption (Ministry of Finance, 2011). AG

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Report for years 2012 and 2013 highlighted that most of the cases reported in the
government agencies were associated with the procurement.
Literature Review
The Malaysian government embarked on the e-Procurement or specifically e-perolehan
project in 1999 to transform the public procurement landscape. Since Malaysian government
is a major purchaser of supplies and services from the private sector spending a total of RM35
billion annually, e-Procurement is seen to be an effective tool of ensuring value for money as
well as transparency and accountability in the public procurement process (Maniam et al.,
2010).
The exercise of e-Procurement has been developed in stages to allow the suppliers
particularly small-scale companies to adapt themselves with the new changes in the
procurement system. The initial stage of implementation of e-Procurement starts with the
launching of two Modules that is the ‘Procurement via Central Contract’ and the ‘Suppliers
Registration’ on October 6, 2000. This was followed by the launching of the ‘Direct Purchase
module’ on May 10, 2002. The module for tender and quotation was the last module
developed under the e-Procurement system.
The ‘e-Procurement’ application enables the suppliers to display and introduce their
products and services virtually over the internet 24 hours a day. In addition, the application
also accommodates the procurement transactions from the initial stage of the procuring
process to the final stage which involves payments to the suppliers and contractors. Among
of the e-Procurement, applications are e-Perolehan, MyProcurement, Direct Tender, and e-
Construction.
As of May 2006, there were 115,000 suppliers registered under the e-Procurement
system in Malaysia (Harian, 2006). To date, the e-Procurement system had generated revenue
of RM 1.08 billion from 107,000 transactions (Harian, 2006). The government issued a
directive TCL 5/2003 instructing all the e-procurement enabled government agencies to
execute procurement transactions through the central contract and direct purchases on-line.
Contractors, on the other hand, need to register under the Contractors Service Centre (CSC)
or Construction Industry Development Board (CIDB) before they can proceed with
government procurement.
Even though construction is mainly a service industry, most of its activities require
material handling and assembly functions. E-Procurement is critical to construction because
it involves a number of partners on each project who all have the need for inventory
management in order not to delay the project or to tie space and money on excess inventory
while also complying with specifications and other variables (Pheng & Meng, 1997). Beyond
the obvious transaction cost savings and access to suppliers, e-Procurement can offer product
standardization, quality assurance, inventory management and the opportunity to manage
material flows down the value chain (i.e. the contractor having input in subcontractors’
choices, the owner having input in contractors’ choices, etc.).
The best practices of the procurement processes in the construction industry are
based on searching for vendor databases and comparing the products based on relevant
technical and cost factors as well as detailed, uniformed, standard documentation (Opentext
News, 2001). Sanders et al., (2001, cited in Issa et al., 2003) determined that e-Procurement
saves up to fifteen percent (15%) of the total purchase cost. It lowers the internal
requisitioning cost by automating the internal requisitioning process. Companies reduce
personnel costs and time inefficiencies with requisition approval and order processing. E-

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Procurement automates the workflow of procurement/resource management processes,
which reduces the cycle time of purchases, decreases stocking requirements, and lowers
inventory management costs. Finally, e-Procurement applications enable enterprises to
manage long-term relationships with suppliers. These relationships can be leveraged to
create an enterprise-wide buying environment with the most favorable conditions.
Significance of the Study
Understanding factors as part of the cause of the system usage has become greater doubt in
the field of Information System (IS). E-procurement is the use of integrated information
technology for part of or all the procurement functions, from the beginning to end, i.e. from
searching, sourcing, negotiating, ordering, and receipt to post-purchase review (Croom &
Jones., 2005; Trkman & McCormack., 2010). E-Procurement is critical to construction because
it involves a number of partners on each project who all have the need for inventory
management in order not to delay the project or to tie space and money on excess inventory
while also complying with specifications and other variables (Pheng & Meng, 1997). Beyond
the obvious transaction cost savings and access to suppliers, e-Procurement can offer product
standardization, quality assurance, inventory management and the opportunity to manage
material flows down the value chain. The e-Procurement also automates the workflow of
procurement/resource management processes, which reduces the cycle time of purchases,
decreases stocking requirements, and lowers inventory management costs. Thus, there is a
need to measure the usage of e-procurement in Malaysian construction, and its impact to
contractors’ performance, which represents the aim of this study to find a validated
instrument measuring the usage and performance of the system.
Materials and Method
The data collection employed in this study is from a self-administered survey questionnaire.
The questionnaire is adapted from two different studies: Vankatesh et al., (2012) and Norzaidi
(2008). The questionnaire was adapted and customized to suit the field of this study and was
distributed to the respondents who were responsible and in charge of the company’s e-
procurement that comprise of company officials - general manager, assistant general
manager, project manager, site manager, engineer, and executive. The survey was composed
from 5 constructs (after the demographical data concerning the respondents): The first four
constructs were related to UTAUT model; first construct: Performance Expectancy (5 items
using the scale of 7). Second construct: Effort Expectancy (5 items using the scale of 7). Third
construct: Social Influence (5 items using the scale of 7). Fourth construct: Facilitating
Condition (6 items using the scale of 7). Fifth construct: E-procurement usage (4 items using
the scale of 7).
Taherdoost (2019) in his study suggests the use of seven-point rating scale and if there
is a need to have respondents to be directed on one side, then he claims that six-point scale
is the most suitable. Accordingly, this study applied the interval scale of 7, in which the
respondents selected a statement among several statements from 1-7 which is considered to
reflect the perceived quality of the subject. Number 1 stands for strongly disagree, while,
number 7 stands for strongly agree. According to Awang et al. (2010; 2012; 2014; 2015) and
Awang et al. (2018), the researcher should apply a Likert Scale without a label because this
measure would give an interval type of data that is continuous and fit the data presumption
for parametric analysis. As per Awang (2010; 2012; 2014; 2015) and Hoque et al. (2017; 2018),
if the analyst adjusted instruments from past studies and altered accordingly, at that point

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the scientist needs to direct both pre-test and pilot-test for these “changed items” so as to
approve them before it tends to be utilized in the final study. Content validity, face validity,
and criterion validity were done as a pre-test for this questionnaire, content validity was done
through content experts, and face validity was done through English language experts,
criterion validity was done through a statistical expert, after these validation tests were
completed, the researcher distributed the instrument to 3 respondents, in order to gather
their comments, and check the consistency in their responses.
After all the required changes according to pre-test results had been done, the
researcher distributed the questionnaire to gather a minimum of 100 responses to be able to
run the exploratory factor analysis (EFA) as according to many researchers for example:
Awang (2010, 2012, 2014, 2015), Hoque et al. (2017, 2018), Noor et al. (2015), Awang et al.
(2018) and Yahaya et al. (2018) claim that EFA should be done for each construct to explore
changes in dimensionality of items from past studies due to changes in the characteristics of
population from the past.
Results and Discussion
The EFA for the First Construct: Performance Expectancy
Performance Expectancy construct was measured by using 5 items namely pe1 till pe5 (Table
1). Every statement of items was measured using Interval Scale ranging between 1 and 7,
where 1 stands for strongly disagree and 7 stands for strongly agree. The mean score and
standard deviation derived for every single item which measured the constructs are shown in
Table 1.
Table 1: Mean and Standard Deviation for items in Performance Expectancy
Items Item statement Mean Std. Deviation
pe1 I find e-procurement useful 5.66 0.867
pe2
Using e-procurement increase my chance of achieving
things that are important to me
5.70 0.936
pe3
Using e-procurement helps me accomplish things more
quickly
5.56 0.938
pe4 Using e-procurement increases my productivity 5.58 0.936
pe5 Overall, I would find e-procurement to be advantageous 5.45 1.011
EFA using Principal Component Analysis as an extraction method performed for these 5 items
to measure performance expectancy of using e-procurement. The results in Table 2 shows
Bartlett’s Test of Sphericity which is Significant since it’s <0.05. Kaiser-Meyer-Olkin Measure
of Sampling Adequacy higher than 0.6 which is for the first construct 0.855, and this means
that the sample size is adequate (Awang, 2010; 2012; 2014; 2015; Hoque et al., 2017; 2018;
and Noor et al., 2015). Accordingly, the current data are acceptable.
Table 2: KMO and Bartlett’s Test score
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
0.855
Bartlett's Test of
Sphericity
Approx. Chi-Square 460.532
Df 10
Sig. 0.000
The scree plot in Figure 1 shows that only one component emerged from the EFA, accordingly
all items in this construct will belong to one component.
Figure 1: Scree Plot for the first construct

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The results in Table 3 the components or dimension for each item is shown in this table, as
it’s clear all items are belonging to one component. The factor loading for every item should
be >0.6 in order to be retained (Awang, 2010; 2012; Awang et al. (2018) and Yahaya et al.,
2018). Thus all items will be retained.
Table 3: Factor Loading Matrix for Performance Expectancy
Items Factor Loading
pe1 0.889
pe2 0.920
pe3 0.837
pe4 0.881
pe5 0.892
Extraction Method: Principal
Component Analysis.
The results in Table 4 show there are one dimension or component emerged from the EFA
procedure based on the computed Eigenvalue >1.0. The total variance explained for
measuring this construct is 78.183%. The total variance explained is acceptable since it
exceeds the minimum 60% (Awang, 2010, 2012; 2014; 2015; Noor et al., 2015; Hoque et al.,
2017; 2018; and Yahaya et al., 2018).
Table 4: Total variance explained
Componen
t
Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative
%
1 3.909 78.183 78.183
Extraction Method: Principal Component Analysis.
The Internal Reliability for the Instrument Measuring Performance Expectancy
The last test that should be done is the internal reliability of each construct. As Table 5 shows
that Cronbach’s Alpha test is 0.929, higher than 0.7, which means that these items are
reliable.

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Table 5: The internal reliability for performance expectancy
Component N of Items Cronbach
Alpha
1 5 0.929
The EFA for the Second Construct: Effort Expectancy
This construct was measured using 5 items listed in Table 6 as ee1 to ee5, and each item was
measured using Likert-scale of 7, where 1 stands for strongly disagree and 7 stands for
strongly agree, the mean response, standard deviation and item statement, for each item,
are listed in Table 6.
Table 6: Mean and Standard Deviation for items in Effort Expectancy
Items Item statement Mean Std.
Deviation
ee1 Learning how to use e-procurement is easy for me 5.37 1.134
ee2 My interaction with e-procurement is clear and understandable 5.44 0.948
ee3 I find e-procurement easy to use 5.50 0.902
ee4 It is easy for me to become skillful at using e-procurement 5.47 0.930
ee5 I find it easy to get e-procurement to do what I want it to do 5.40 0.846
EFA using principal component analysis as an extraction method performed for these 5 items
to measure effort expectancy of using e-procurement. The results in Table 7 shows Bartlett’s
Test of Sphericity which is Significant since it’s <0.05. Kaiser- Meyer-Olkin measure of
sampling Adequacy higher than 0.6 which is for the 2nd construct 0.847, and this means that
the sample size is adequate (Awang, 2010; 2012; 2014; 2015; Hoque et al., 2017, 2018; Noor
et al., 2015). Accordingly, the current data are acceptable.
Table 7: KMO and Bartlett’s Test score
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
0.847
Bartlett's Test of
Sphericity
Approx. Chi-Square 422.524
Df 10
Sig. 0.000
The scree plot in Figure 2 shows that only one component is emerged from the EFA,
accordingly all items in this construct will belong to one component.

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Figure 2: The Scree Plot for the second construct
The results of the components or dimension for each item is shown in Table 8, as it’s clear all
items are belonging to one component. The factor loading for every item should be >0.6 in
order to be retained (Awang, 2010; 2012; 2014; 2015; Awang et al., 2018 and Yahaya et al.,
2018). Thus all items will be retained.
Table 8: Factor Loading Matrix for Effort Expectancy
Items Factor loading
ee1 0.688
ee2 0.905
ee3 0.914
ee4 0.915
ee5 0.835
Extraction Method: Principal
Component Analysis.
The results in Table 9 show there are one dimension or component emerged from the EFA
procedure based on the computed Eigenvalue >1.0. The total variance explained for
measuring this construct is 73.217%. The total variance explained is acceptable since it
exceeds the minimum 60% (Awang, 2010; 2012; 2014; 2015; Noor et al., 2015; Hoque et al.,
2017, 2018; and Yahaya et al., 2018).
Table 9: Total variance explained
Componen
t
Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative
%
1 3.661 73.217 73.217
Extraction Method: Principal Component Analysis.
The Internal Reliability for the Instrument Measuring Effort Expectancy
The last test that should be done is the internal reliability of each construct. As Table 10 shows
that Cronbach’s Alpha test is 0.898, higher than 0.7, which means that these items are
reliable.

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Table 10: The internal reliability for effort expectancy
Component N of Items Cronbach
Alpha
1 5 0.898
The EFA for the Third Construct: Social Influence
This construct was measured using 5 items listed in Table 11 as si1 to si5, and each item was
measured using Likert-scale of 7, where 1 stands for strongly disagree and 7 stands for
strongly agree, the mean response, standard deviation and item statement, for each item,
are listed in Table 11.
Table 11: Mean and Standard Deviation for items in Social Influence
Items Item statement Mean Std.
Deviation
si1
People who are important to me think that I should use e-
procurement
5.58 0.772
si2
People who influence my behavior think that I should e-
procurement
5.76 0.708
si3
People whose opinions that I value prefer that I use e-
procurement
5.30 0.848
si4
Friend’s suggestion and recommendation will affect my decision
to use e-procurement
5.65 0.750
si5
I would use e-procurement because the proportion of my
friends use it
5.17 1.070
EFA using principal component analysis as an extraction method performed for these 5 items
to measure social influence of using e-procurement. The results in Table 12 shows Bartlett’s
Test of Sphericity which is significant since it’s <0.05. Kaiser- Meyer-Olkin measure of sampling
Adequacy higher than 0.6 which is for the third construct 0.787, and this means that the
sample size is adequate (Awang, 2010; 2012; 2014; 2015; Hoque et al., 2017, 2018; Noor et
al., 2015). Accordingly, the current data are acceptable.
Table 12: KMO and Bartlett’s Test score
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
0.787
Bartlett's Test of
Sphericity
Approx. Chi-Square 209.178
Df 10
Sig. 0.000
The scree plot in Figure 3 shows that only one component is emerged from the EFA,
accordingly all items in this construct will belong to one component.
Figure 3: The Scree Plot for the third construct

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The results in Table 13, the components or dimension for each item is shown in this table, as
it’s clear all items are belonging to one component. The factor loading for every item should
be >0.6 in order to be retained (Awang, 2010; 2012; 2014; 2015; Awang et al., 2018 and
Yahaya et al., 2018). Thus all items will be retained.
Table 13: Factor Loading Matrix for Social Influence
Items Factor loading
si1 0.754
si2 0.823
si3 0.796
si4 0.811
si5 0.646
Extraction Method: Principal
Component Analysis.
The results in Table 14 show there are one dimension or component emerged from the EFA
procedure based on the computed Eigenvalue >1.0. The total variance explained for
measuring this construct is 62.123%. The total variance explained is acceptable since it
exceeds the minimum 60% (Awang, 2010; 2012; 2014; 2015; Noor et al., 2015; Hoque et al.,
2017, 2018; and Yahaya et al., 2018).
Table 14: Total variance explained
Componen
t
Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative
%
1 2.956 62.123 62.123
Extraction Method: Principal Component Analysis.
The Internal Reliability for the Instrument measuring Social Influence
The last test that should be done is the internal reliability of each construct. As Table 15 shows
that Cronbach’s Alpha test is 0.809, higher than 0.7, which means that these items are
reliable.

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Table 15: The internal reliability for social influence
Component N of Items Cronbach
Alpha
1 5 0.809
The EFA for the Fourth Construct: Facilitating Condition
This construct was measured using 5 items listed in Table 16 as fc1 to fc5, and each item was
measured using Likert-scale of 7, where 1 stands for strongly disagree and 7 stands for
strongly agree, the mean response, standard deviation and item statement, for each item,
are listed in Table 16.
Table 16: Mean and Standard Deviation for items in Facilitating Condition
Items Item statement Mean Std.
Deviation
fc1 I have the resources necessary to e-procurement 5.59 0.760
fc2 I have the knowledge necessary to use e-procurement 5.69 0.742
fc3 E-procurement is compatible with other technologies I use 5.53 0.729
fc4
I can get help from others when I have difficulties using e-
procurement
5.51 0.799
fc5 Using e-procurement is entirely within my control 5.65 0.761
EFA using principal component analysis as an extraction method performed for these 5 items
to measure facilitating condition of using e-procurement. The results in Table 17 shows
Bartlett’s Test of Sphericity which is Significant since it’s <0.05. Kaiser- Meyer-Olkin measure
of sampling Adequacy higher than 0.6 which is for the fourth construct 0.810, and this means
that the sample size is adequate (Awang, 2010; 2012; 2014; 2015; Hoque et al., 2017, 2018;
Noor et al., 2015). Accordingly, the current data are acceptable.
Table 17: KMO and Bartlett’s Test score
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
0.810
Bartlett's Test of
Sphericity
Approx. Chi-Square 322.654
Df 10
Sig. 0.000
The scree plot in Figure 4 shows that only one component is emerged from the EFA,
accordingly all items in this construct will belong to one component.

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Figure 4: The Scree Plot for the fourth construct
The results in Table 18 the components or dimension for each item is shown in this table, as
it’s clear all items are belonging to one component. The factor loading for every item should
be >0.6 in order to be retained (Awang, 2010; 2012; 2014; 2015; Awang et al., 2018 and
Yahaya et al., 2018). Thus, all items will be retained.
Table 18: Factor Loading Matrix for Facilitating Condition
Items Factor loading
fc1 0.881
fc2 0.823
fc3 0.765
fc4 0.845
fc5 0.803
Extraction Method: Principal
Component Analysis.
The results in Table 19 show there are one dimension or component emerged from the EFA
procedure based on the computed Eigenvalue >1.0. The total variance explained for
measuring this construct is 67.955%. The total variance explained is acceptable since it
exceeds the minimum 60% (Awang, 2010; 2012; 2014; 2015; Noor et al., 2015; Hoque et al.,
2017, 2018; and Yahaya et al., 2018).
Table 19: Total variance explained
Componen
t
Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative
%
1 3.398 67.955 67.955
Extraction Method: Principal Component Analysis.
The internal reliability for the instrument measuring Facilitating Condition
The last test that should be done is the internal reliability of each construct. As Table 20 shows
that Cronbach’s Alpha test is 0.880, higher than 0.7, which means that these items are
reliable.

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Table 20: The internal reliability for facilitating condition
Component N of Items Cronbach
Alpha
1 5 0.880
The EFA for the Fifth Construct: e-procurement Usage
This construct was measured using 4 items listed in Table 21 as pu1 to pu5, and each item
was measured using Likert-scale of 7, where 1 stands for strongly disagree and 7 stands for
strongly agree, the mean response, standard deviation and item statement, for each item,
are listed in Table 21.
Table 21: Mean and Standard Deviation for items in e-procurement Usage
Items Item statement Mean Std.
Deviation
pu1 I use e-procurement a lot to do my work 5.97 0.648
pu2 I use e-procurement whenever possible to do my work 6.03 0.648
pu3 I use e-procurement frequently to do my work 6.06 0.611
pu4 I use e-procurement whenever appropriate to do my work 5.84 0.801
EFA using principal component analysis as an extraction method performed for these 4 items
to measure e-procurement usage of using e-procurement. The results in Table 22 shows
Bartlett’s Test of Sphericity which is Significant since it’s <0.05. Kaiser- Meyer-Olkin measure
of sampling Adequacy higher than 0.6 which is for the fifth construct 0.793, and this means
that the sample size is adequate (Awang, 2010; 2012; 2014; 2015; Hoque et al., 2017, 2018;
Noor et al., 2015). Accordingly, the current data are acceptable.
Table 22: KMO and Bartlett’s Test score
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
0.793
Bartlett's Test of
Sphericity
Approx. Chi-Square 223.678
Df 6
Sig. 0.000
The scree plot in Figure 5 shows that only one component is emerged from the EFA,
accordingly all items in this construct will belong to one component.
Figure 5: The Scree Plot for the fifth construct

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The results in Table 23 the components or dimension for each item is shown in this table, as
it’s clear all items are belonging to one component. The factor loading for every item should
be >0.6 in order to be retained (Awang, 2010; 2012; 2014; 2015; Awang et al., 2018 and
Yahaya et al., 2018). Thus all items will be retained.
Table 23: Factor Loading Matrix for e-procurement Usage
Items Factor loading
pu1 0.820
pu2 0.916
pu3 0.855
pu4 0.773
Extraction Method: Principal
Component Analysis.
The results in Table 24 show there are one dimension or component emerged from the EFA
procedure based on the computed Eigenvalue >1.0. The total variance explained for
measuring this construct is 70.973%. The total variance explained is acceptable since it
exceeds the minimum 60% (Awang, 2010; 2012; 2014; 2015; Noor et al., 2015; Hoque et al.,
2017, 2018; and Yahaya et al., 2018; Khalid, 2020).
Table 24: Total variance explained
Componen
t
Extraction Sums of Squared Loadings
Total % of
Variance
Cumulative
%
1 2.839 70.973 70.973
Extraction Method: Principal Component Analysis.
The internal reliability for the instrument measuring e-procurement Usage
The last test that should be done is the internal reliability of each construct. As Table 20 shows
that Cronbach’s Alpha test is 0.880, higher than 0.7, which means that these items are
reliable.
Table 25: The internal reliability for facilitating condition
Component N of Items Cronbach
Alpha
1 4 0.854
Conclusion
The current research adds a remarkable contribution to the measurement of the 5 constructs,
mainly in the e-procurement context. The results show that every of the five constructs have
one component or dimension, the factor loading for every item exceeds the minimum
threshold of 0.6, with high Cronbach's Alpha value, meet Bartlet Test achievements
(significant) for all the constructs, Kaiser-Meyer-Olkin Measure of Sampling Adequacy was
higher than 0.6 for all the constructs, and factor loading exceeds the minimum threshold of
0.6. Hence, this study found a valid and reliable instrument for measuring the usage of e-
procurement to Malaysian contractors’. Therefore, this instrument can be used to measure
the usage of e-procurement in the targeted organizations in this study.

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However, it is recommended during field study that some precautions need to be done to
ensure that the data collection are free from response bias and common method variance
and therefore, would generate significant findings.
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